A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables

Jia Chen, Degui Li, Oliver Linton

Research output: Contribution to journalArticlepeer-review

Abstract


This paper studies the estimation of large dynamic covariance matrices with multiple conditioning variables. We introduce an easy-to-implement semiparametric method to estimate each entry of the covariance matrix via model averaging marginal regression, and then apply a shrinkage technique to obtain the dynamic covariance matrix estimation. Under some regularity conditions, we derive the asymptotic properties for the proposed estimators including the uniform consistency with general convergence rates. We further consider extending our methodology to deal with the scenarios: (i) the number of conditioning variables is divergent as the sample size increases, and (ii) the large covariance matrix is conditionally sparse relative to contemporaneous market factors. We provide a simulation study that illustrates the finite-sample performance of the developed methodology. We also provide an application to financial portfolio choice from daily stock returns.
Original languageEnglish
Pages (from-to)155-176
Number of pages22
JournalJournal of Econometrics
Volume212
Issue number1
Early online date12 Apr 2019
DOIs
Publication statusPublished - 1 Sep 2019

Bibliographical note

© 2019 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

Keywords

  • Dynamic covariance matrix
  • MAMAR
  • Semiparametric estimation
  • Sparsity
  • Uniform consistency

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